System and method for fast charging of lithium-ion batteries

ABSTRACT

A Li-ion battery management system comprises a Li-ion cell, one or more sensors configured to sense one or more operating conditions of the Li-ion cell, a controller having non-transitory memory for storing machine instructions that are to be executed by the controller and operatively connected to the Li-ion cell, the machine instructions when executed by the controller implement the following functions: receive the one or more operating conditions and a trained machine learning (ML) model, and output an indicator value along a state of charge (SOC) trajectory in response to the one or more operating conditions and the trained ML model to control a fast charge state of the Li-ion cell from a current source.

TECHNICAL FIELD

The present disclosure relates to a system and a method of fast chargingan electrochemical cell such as a Li-ion battery while utilizing amachine learning (ML) model.

BACKGROUND

Lithium-ion batteries have become common and their use widespread. Fastcharging of lithium-ion batteries is one of the technical challengesfaced by companies providing various devices that use these batteriessuch as consumer electronics, power tools, and electric vehicles.Consumers generally prefer a minimal wait time for recharging theirsmartphone, power drill, electric vehicle, or other electronic device.Hence, battery suppliers and device manufacturers are in competition todeliver devices that charge as quickly as possible. Yet, fast chargingmay result in overpotential and mechanical stress in the battery thatcan accelerate the aging process of the battery and result in reducedlifetime.

SUMMARY

According to one embodiment, a Li-ion battery management system isdisclosed. The system includes a Li-ion cell and one or more sensorsconfigured to sense one or more operating conditions of the Li-ion cell.The system also includes a controller having non-transitory memory forstoring machine instructions that are to be executed by the controllerand operatively connected to the Li-ion cell, the machine instructionswhen executed by the controller implement the following functions:receive the one or more operating conditions and a trained machinelearning (ML) model; and output an indicator value along a state ofcharge (SOC) trajectory in response to the one or more operatingconditions and the trained ML model to control a fast charge state ofthe Li-ion cell from a current source. The SOC trajectory may include aconstant current (CC) phase, a constant indicator (CI) phase, and/or aconstant voltage (CV) charge phase. The indicator value may be a stateof the cell along the SOC trajectory. The indicator value may be anoverpotential value. The one or more operating conditions may includeone or more of an internal cell temperature, a cell voltage and acurrent. The machine instructions when executed by the controller mayfurther implement the following function: determine a first indicatorvalue at time (t). The machine instructions when executed by thecontroller may further implement the following function: determine asecond indicator value at time (t+1). The trained ML model may include atraining data set including a plurality of individual chargetrajectories, and each trajectory is generated for a specificcombination of model parameters, initial conditions, and/or ambienttemperatures. The trained ML model may be a physics-based trained model.

In an alternative embodiment, a method of fast charging a Li-ion batterycell is disclosed. The method may include sensing one or more operatingconditions of the Li-ion cell, receiving the one or more operatingconditions and a trained machine learning (ML) model, and outputting anindicator value along a state of charge (SOC) trajectory in response tothe one or more operating conditions and the trained ML model to controla fast charge state of the Li-ion cell from a current source. The SOCtrajectory may include a constant current (CC) phase, a constantindicator (CI) phase, and/or a constant voltage (CV) charge phase. Theindicator value may be a state of the cell along the SOC trajectory. Theindicator value may be an overpotential value. The one or more operatingconditions may include one or more of an internal cell temperature, acell voltage, and a current. The trained ML model may include a trainingset including a plurality of individual charge trajectories, and furthercomprising generating each trajectory for a specific combination ofmodel parameters, initial conditions, and/or ambient temperatures.

In another embodiment, a method of estimating a battery target indicatorvalue for a Li-ion cell is disclosed. The method may include training amachine learning (ML) model including a training data set including: aplurality of individual charge trajectories, wherein each trajectory isgenerated for a specific combination of model parameters, initialconditions, and/or ambient temperatures, a charge phase variable, ameasurement data set, and an indicator variable. The method may alsoinclude sensing one or more operating conditions of the Li-ion cell,receiving the one or more operating conditions and the trained ML model,and outputting an indicator value along a state of charge (SOC)trajectory in response to the one or more operating conditions and thetrained ML model. The indicator value may be a state of the cell alongthe SOC trajectory. The method may also include supplying the trainingdata set from a physics-based model. The measurement data set mayinclude one or more operating conditions of the Li-ion cell. The one ormore operating conditions may include one or more of an internal celltemperature, a cell voltage, and a current.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a voltage profile during constant-current constant-voltage(CC-CV) charge of a lithium-ion battery;

FIG. 2 shows a comparison of a conventional CC-CV voltage profile with aphysics-based estimator method voltage profile;

FIG. 3 is a schematic depiction of a non-limiting example of a systemfor fast charging of a Li-ion battery cell according to one or moreembodiments disclosed herein;

FIG. 4 shows a schematic illustration of the training steps of the MLmodel disclosed herein; and

FIG. 5 is a schematic depiction of a non-limiting example of a set ofprocess steps disclosed herein.

DETAILED DESCRIPTION

Embodiments of the present disclosure are described herein. It is to beunderstood, however, that the disclosed embodiments are merely examplesand other embodiments can take various and alternative forms. Thefigures are not necessarily to scale; some features could be exaggeratedor minimized to show details of particular components. Therefore,specific structural and functional details disclosed herein are not tobe interpreted as limiting, but merely as a representative basis forteaching one skilled in the art to variously employ the embodiments. Asthose of ordinary skill in the art will understand, various featuresillustrated and described with reference to any one of the figures canbe combined with features illustrated in one or more other figures toproduce embodiments that are not explicitly illustrated or described.The combinations of features illustrated provide representativeembodiments for typical applications. Various combinations andmodifications of the features consistent with the teachings of thisdisclosure, however, could be desired for particular applications orimplementations.

Except where expressly indicated, all numerical quantities in thisdescription indicating dimensions or material properties are to beunderstood as modified by the word “about” in describing the broadestscope of the present disclosure.

Reference is being made in detail to compositions, embodiments, andmethods of embodiments known to the inventors. However, it should beunderstood that the disclosed embodiments are merely exemplary of thepresent invention which may be embodied in various and alternativeforms. Therefore, specific details disclosed herein are not to beinterpreted as limiting, rather merely as representative bases forteaching one skilled in the art to variously employ the presentinvention.

The description of a group or class of materials as suitable for a givenpurpose in connection with one or more embodiments implies that mixturesof any two or more of the members of the group or class are suitable.Description of constituents in chemical terms refers to the constituentsat the time of addition to any combination specified in the description,and does not necessarily preclude chemical interactions amongconstituents of the mixture once mixed. The first definition of anacronym or other abbreviation applies to all subsequent uses herein ofthe same abbreviation and applies mutatis mutandis to normal grammaticalvariations of the initially defined abbreviation. Unless expresslystated to the contrary, measurement of a property is determined by thesame technique as previously or later referenced for the same property.

Lithium ion batteries have become a staple among rechargeable batteries.Yet, despite their prevalence, lithium ion batteries face a variety ofchallenges. For example, charging of a Li-ion battery is, in mostapplications, a relatively demanding regiment experienced by thebattery. The charging process is a significant contributor for aging anddegradation of a Li-ion cell. The degradation of a Li-ion cell ispredominantly caused by side-reactions such as lithium plating, whichconsumes cyclable lithium. Overpotential determines occurrence of theharmful side-reactions. If the overpotential could be correctlyestimated, an algorithm could be developed to minimize theside-reactions inside of the Li-ion cell, thus maintaining intendedlife-time of the battery.

There have been many attempts to charge Li-ion cells in minimal timewhile trying to minimize their degradation. In general, these methodscan be divided in two groups: (1) methods which use only availablereal-time measurements from the cell, and (2) methods that modelinternal processes of the cell and use estimates of internal variablesto control the charging process.

One of the most common charge methods in the industry that uses currentand voltage measurements is called constant-current constant-voltagemethod. FIG. 1 shows a characteristic charge voltage profile of theCC-CV method. As can be seen in FIG. 1, the voltage profile includes twophases. The first phase corresponds to a section on the profile where abattery management system (BMS) maintains charge current at constantvalue. The second phase corresponds to a section where charge current isregulated (reduced) by the BMS to maintain the cell's voltage atconstant value. The corresponding current and voltage thresholds used byBMS to control the current allows a designer to tune BMS controller tobe more or less aggressive depending on the application. Setting higherthreshold values results in faster charging as it allows higher currentintegral over the same period of time. But such faster charging stressesthe Li-ion cell more and may result in faster degradation if thethresholds are set too high. Hence, this method allows a tradeoffbetween minimizing the charge time and extending the Li-ion battery'slife.

In an ideal battery, and without limitation of the charging unit, onecould pass all the charge needed to bring a battery from one state ofcharge (SOC) to another SOC instantaneously. Kinetic limitations in realbatteries, however, allow only a finite current to be passed through abattery. Many internal processes of the battery have an influence on thecharge transfer capabilities, e.g. finite diffusion rate of lithium ionsin the electrolyte, reduction/oxidation of materials other than theactive material, formation of resistive films on the active particlesurface, and charge transfer limitation between the electrolyte and theactive material. The faster the charge transfer is forced to happen, themore strongly these processes affect the health of the battery. Cellmanufacturers thus always provide additional information aboututilization constraints on their cells. These constraints mostly involvelimits on the maximum charge or discharge current, limits of lower andupper cut-off voltages, and the operating temperature domain. Somemanufacturers provide these limits at different operating ambienttemperatures. All these limits are geared towards the CC/CV chargingmethod, and hence are rather conservative, since the limits arespecified for the complete lifetime of the battery.

Research has shown that even though current and voltage thresholds maybe used as adequate proxies for the amount of degradation-causing stressin the cell at low and high states of charge (SOC), at medium SOC, thecurrent and voltage thresholds are too conservative and may result inunnecessary extension of the charge process. Alternatively, if thecurrent and voltage thresholds are set too high, they may induceaccelerated degradation by causing high stresses towards the ends oftheir respective phases.

SOC may be defined as the percentage of the remaining charge inside abattery to the full charge, ranging between about 0% and 100%. SOCprovides information regarding performance of the battery andinformation when the battery should be recharged. Furthermore, the BMSmay use SOC information for power management. Accurate SOC informationis thus critical, especially in some applications such as electricvehicles, where consumers rely on the SOC information to determine theirdriving range.

It has been shown that standard charging techniques such as CC-CV, ifused for fast charging, can result in damage to the battery due to thelarge currents passed through the battery. These large currents resultin dangerous overpotentials and mechanical stress in the battery thatcauses the battery to age fast resulting in reduced lifetime.

More optimal charge methods have been proposed such as a method thatutilizes a real-time estimate of internal battery states. The internalbattery states serve as indicators of underlying aging mechanisms.Examples of internal states that may serve as indicators of cell agingmay include the overpotential for the Li-plating reaction in the anode,overpotential for electrolyte degradation in the cathode, or both. Whenthresholds on such indicator variables are used in addition to thecurrent and voltage thresholds by the BMS, the charge process mayinclude more than two phases. FIG. 2 shows an example of the chargetrajectory that includes three phases. The first and the last phases arethe CC and CV charge phases, correspondingly. The second phase isdefined by the BMS controller regulating charge current to maintain theindicator variable at the desired threshold. The respective phase CI isa constant indicator phase. Similar to the current and voltagethresholds, the threshold of the indicator variable provides for atradeoff between the speed of charge or degradation.

The method utilizing indicator variables provides a more optimal resultthan the traditional CC-CV method by allowing faster charging with thesame rate of degradation or extends battery lifetime while maintainingcharge duration. The method utilizes a physics-based Lithium-ion cellstate estimator. The estimator provides a number of estimates to afeedback controller which may adjust charge current based on thereceived estimates. The feedback controller may switch from one phase toanother, choosing between various factors limiting charge current basedon the implemented control logic and inputs. The inputs may include atleast one or a set of indicator variables and measurements.

As a result, the indicator variables may be actively controlled by thefeedback controller such that their values do not drop below a thresholdvalue, thus preventing a charge regime with accelerated aging. Suchaging regime is visible in FIG. 2 for the conventional CC-CV charge, inwhich the indicator variable dropped below zero. Unlike the conventionalCC-CV voltage profile, the voltage profile does not dip below zero dueto limiting of the indicator variable within a certain range when thephysics-based estimator was used.

But the estimator-method is relatively computationally expensive becauseit requires real-time estimation of the cell's internal states with acomplex electrochemical model. Real-time estimates of the states can beprovided by the state estimator which represents mathematical model ofthe physical processes taking place in the cell during charge. Themathematical model includes a set of ordinary differential equationswith algebraic constraints. A typical Li-ion cell estimator based onsuch an electrochemical model may have over about 70 dynamic states andsince, typically, only a few of them are used by the charging controlleras indicator variables during fast charge, the method has certaincomputational inefficiencies, especially where many cells are usedsimultaneously such as in an electric vehicle battery pack.

Thus, there is a need for an optimized charge method, especially a fastcharge method, which would be efficient, not computationally expensive,and providing a fast charge while maintaining the expected orpredetermined battery lifetime.

In one of more embodiments, a system for a fast charge of anelectrochemical cell is disclosed. Without limiting the disclosure to asingle definition, a fast charge may be characterized as a process ofsupplying a charge current to a battery cell with the target to increaseits SOC to a desired value in minimal amount of time while respectingcell's operating constraints on voltage, current, temperature, and othervariables. The charge time period may vary from several seconds toseveral minutes, or hours. A non-limiting example charge time period maybe about 10 to 20 minutes, 11 to 18 minutes, or 12-15 minutes such as 8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 minutes long.

The disclosed system and methods include an ML model instead of or inplace of a physics-based estimator. The ML model allows to condensefunctionality of the physics-based estimator. The ML model may betrained offline, thus reducing computational demand of the BMS, reducingcost, increasing efficiency of the fast charge process.

FIG. 3 shows a block diagram schematically illustrating a system 100including a BMS 102 controlling the fast charge process of anelectrochemical cell, specifically a Li-ion cell 103, and including anML model 104. The system 100 may also include one or more of acontroller 108 with memory 110, one or more sensors 112, a currentsource (not depicted), and a model 106 designed to train the ML model104 offline, as is discussed below. The BMS 102 is operatively connectedto the cell 103.

The ML model 104 may be Linear Regression, Logistic Regression,K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Trees(DT), ensembles, or Artificial Neural Network (ANN). The ML model 104may be embedded in the BMS 102.

During fast charge, the BMS 102 may use the ML model 104 to estimate atarget variable to generate a control output. Specifically, the BMS 102uses the ML model 104 to estimate an optimal or ideal amount(s) ofcurrent to be supplied to the cell 103 during the entire duration of afast charge. The optimal or ideal amount of current may vary in timeduring the charging process. The optimal or ideal amount of current isan amount of current that results in a minimal duration of or a timeperiod required for a fast charge while achieving minimal damage to thecell as a result of the charging process.

The ML model 104 is trained offline based on as complete as possible ofa set of input data. Accordingly, the ML model 104 is supplied with atraining data set and trained based on the data set. The training dataset may be generated by a model 106 parametrized and fitted toexperimental data. Training of the ML model 104 offline via model 106 isschematically depicted in FIG. 4.

The model 106 may be a physics-based model. The model 106 may be asimulation model capable of simulating internal states of the cell 103.The model 106 may be an electrochemical model providing battery stateestimates over a lifetime of the battery. The model 106 may be any modelcapable of simulating the electrochemical processes of the Li-ion cell.The model 106 may determine the state of a battery based on thebattery's degradation level. The model 106 may be capable of predictingfuture battery states under a possible future charging sequence andevaluating charging performance over an extended time period. The model106 may approximate nonlinear system of partial differential equationsmodeling the battery's electrochemical processes into a linearizedsystem of algebraic equations. The model may be a dualfoil model usingmass transfer, diffusion, migration, and reaction kinetics to model theelectrochemical reactions in the Li-ions cell. The model 106 may includea state filter algorithm such as a Kalman filter for state estimations.The model 106 may be a simplified and computationally efficient model.The model 106 may be an equivalent circuit model. The model 106 may ormay not be embedded in the BMS 102.

The training data set generated by the model 106 may include SOCtrajectories corresponding to one or more, preferably all expectedenvironmental conditions and/or initial states of the cell 103 fromwhich the charge is initiated. The training data set may further includeSOC trajectories corresponding to various ages of the cell 103 and/orpossible variations of cell parameters that influence the cell'sbehavior during the charge process.

More specifically, the training data set may include a plurality or acollection of individual SOC trajectories, where each trajectory isgenerated for a specific combination of model parameters, initialcondition, and ambient temperature.

Since the chemistry of each Li-ion cell 103 changes in time and withuse, the Li-ion cell 103 may have different requirements for optimalcharging throughout different stages in its lifetime. The ML model 104is thus trained on or provided data set for different ages and stages oflife of the Li-ions cell 103. Varying of the model parameters thusresults in charge trajectories corresponding to various ages of theconsidered Li-ion cell 103 from the beginning of life to end of itsexpected life. Variation of model parameters also emulates naturaldistribution of the cell's properties due to inaccuracies inmanufacturing processes.

Varying initial conditions from which the charge starts such as thecell's SOC or initial temperature captures the cell's dependency of thecharge trajectories on the initial states. Varying ambient temperatureresults in capturing the influence of environment on the chargetrajectories. Ambient temperature, just like internal temperature, has asignificant impact on performance of the Li-ion cells and limits theirapplication at both low and high temperature ranges. At temperatureslower than an optimal range of about −20° C. to 60° C., chemicalreaction activity and charge-transfer velocity may slow down, leading todecreased ionic conductivity in the electrolyte(s) and lithium-iondiffusivity in the electrode(s). The low temperatures may thus result inreduced energy and power capabilities of the Li-ion cell. Temperatureshigher than the optimal range may likewise lead to loss of capacity dueto loss of lithium and reduction of active materials and powerperformance due to increase of internal resistance. Since thetemperature information may influence quality of the cell 103 and itslifetime, the initial temperature as well as ambient temperatureinformation are captured in the individual charge trajectories of thetraining data.

The training data may also include a charge phase variable, a variableindicating in which phase of the charge process, such as CC, CI, or CVfor example, the cell 103 is. The SOC trajectory may include a CC phase,a CI phase, and/or a CV phase.

Besides the training data set for different ages and stages of life ofthe Li-ions cell 103, the ML model 104 is supplied one or more operatingconditions or measured data inputs as an offline measurement data setregarding the cell 103. The one or more operating conditions or offlinemeasurement data set may include cell voltage, current, internaltemperature, capacity, ambient temperature, the like, or a combinationthereof.

The model 106 also feeds a target indicator variable range to the MLmodel 104 offline. The variable is not measurable. The indicatorvariable is an estimate of an internal battery state such as reactionoverpotential or a state of the cell along the SOC trajectory. Theindicator variable and indicator value may vary based on modelparameters, initial condition, ambient temperature, the like, or acombination thereof.

After the training data set is provided by the physics-based model 106to the ML model 104 offline, and the ML model 104 is trained, the MLmodel 104 is brought or connected online. While online, the ML model 104may be supplied real time input measurements of the cell or one or moreoperating conditions such as cell voltage, current, internaltemperature, capacity, ambient temperature, the like, or a combinationthereof. Based on the training data set, and/or the measurements, the MLmodel 104 is capable of estimating the indicator variable within thetarget range. The ML model 104 may accurately estimate the indicatoralong at least one or all possible charging trajectories. In otherwords, the ML model 104 may generate an indicator estimate value alongthe cell's charging trajectory.

In turn, the ML model 104 may provide an output, which is the estimateof the indicator value to the controller 108 in real time along thecharge trajectory. The estimate of the indicator value may serve as aninput for the controller 108. In response to a receipt of new inputssuch as one or more operating conditions or real time measurements, theML model 104 may generate a new estimated indicator value within thetarget and provide the same to the controller 108. The generating and/orsupplying may happen on a continuous, discontinuous, periodical, orrandom basis.

The variable may change continuously, discontinuously, periodically, ina regular or irregular interval. A new estimate may be generated by theML model 104 and provided to the controller 108 continuously,discontinuously, periodically, in a regular or irregular interval. Anon-limiting example of the interval may be a millisecond, a second,etc.

The trained ML model 104 may be incorporated into the controller 108.The trained ML model 104 may serve as an input for the controller 108.The controller 108 may output the indicator value.

The controller 108 executes machine instructions stored in thecontroller's memory 110. The machine instructions may implement thefollowing functions: receive the one or more operating conditions,receive the trained ML model, output an indicator value along an SOCtrajectory in response to the one or more operating conditions and thetrained ML model to control a fast charge state of the Li-ion cell froma current source, determine a first indicator value at time (t),determine a second indicator value at time (t+1), or the like.

The controller 108 may output an indicator value along an SOC trajectoryand/or an amount of current to be provided to the cell 103 during thecharging process, specifically during one or more stages of the chargingprocess. The control output may include the amount of current to besupplied in a predetermined time period to the Li-ion cell 103 toachieve the ideal or optimal charge or fast charge.

The ML model 104 is in communication with a controller 108, which may beembedded in the BMS 102 together with the ML model 104. The controller108 may be operably connected to a current source (not depicted) towhich the controller 108 supplies a control output. The control outputmay include initiating or stopping a supply of current from the currentsource to the cell 103, increasing, decreasing, adjusting, ormaintaining an amount of current to be provided to the cell 103 at anygiven moment during the charge time period or during the duration of thefast charge, or both.

The controller 108 includes one or more hardware and software componentsand may be implemented as a digital control device that executes storedprogram instructions that are stored in its non-transitory memory 110.The controller 108 may be implemented as a digital microcontroller, butin alternative embodiments, the controller 108 is, for example, ageneral purpose microprocessor, field programmable gate array (FPGA),application specific integrated circuit (ASIC), a digital signalprocessor (DSP), or any other suitable digital processor thatincorporates hardware and software components to enable monitoring ofthe electrochemical cell 103 and control of the level of input currentthat is applied to the electrochemical cell 103 during a fast chargeprocess.

The controller 108 may receive the following inputs: one or moreoperating conditions or real time measurements from the one or moresensors 112, a trained ML model 104, estimates of the indicator from theML model 104, additional inputs such as predetermined constraintparameters that are used to limit the input current to the cell 103,data corresponding to predetermined physical, chemical, andelectrochemical properties of the cell 103, additional data providedfrom one or more external sources, the like, or a combination thereof.In response to a receipt of new inputs such as a new estimated indicatorvalue or one or more operating conditions, the controller 108 may outputa new indicator value, match the estimated indicator value to a target,generate a new control output, supply the same to a current source, thecell 103, or both, or the like. The outputting, matching, generating,and/or supplying may happen on a continuous, discontinuous, orperiodical basis.

The memory 110 includes one or more digital data storage devicesincluding, but not limited to, random access memory (RAM), solid-statestorage memory including NAND and NOR flash memories or EEPROM memory,magnetic and optical data storage media, and the like. The memory 110may also store data corresponding to predetermined physical, chemical,and electrochemical properties of the electrochemical cell 103, themeasurements of current, voltage, or temperature of the cell 103, orambient temperature, the like, or a combination thereof. The memory 110may be non-transitory memory. The memory 110 may also store the ML model104 data corresponding to the estimated target indicator variable,predetermined constraint parameters that are used to limit the inputcurrent to the electrochemical cell 103, sensor data received from oneor more sensors 112 in the system 100, the current source data, machineinstructions that are to be executed by the controller 108.

The system 100 may include one or more sensors 112 such as the cell 103current sensor, voltage sensor, ambient temperature sensor, cellinternal temperature sensor, one or more additional sensors, or acombination thereof. The one or more sensors 112 may be located withinthe cell 103, on the outer surface of the cell 103, or both. The one ormore sensors 112 may sense one or more operating conditions, ameasurement data or data set, provide the one or more operatingconditions or measurement data or data set as input to the controller108, the ML model 104, and/or BMS in real time, offline, or both. Thedata from the one or more sensors 112 may be used by the controller 108and/or the ML model 104 to generate the target indicator variable value.

The battery cell 103 includes two electrodes that are electricallyconnected to a current source to enable the current source to deliver aninput electric current that charges the cell 103. The cell 103 mayinclude multiple electrochemical cells. A battery cell 103 may includeone or more electrochemical cells that are integrated into a singlephysical package with two electrical terminals that receive current froman external current source during the fast charging operation. Thebattery package may optionally include one or more of the controller 108and sensors 112 to control the charging process of the battery.

In one or more embodiments, a fast charge process of an electrochemicalcell 103 is described herein. The method may include generating trainingdata and training the ML model 104 offline. The trained ML model 104 maybe connected online to accurately estimate an indicator value along atleast one charging trajectory. The method may include offline generatingthe data set that covers all possible charging trajectories as discussedabove.

The method may include the disclosed model development includingselection of machine learning model architecture and model inputs. AsFIG. 4 indicates, the method may include offline generating of atraining data set by the physics-based model 106 capable of producingexperimentally verified estimate of the desired indicator variable valuein response to provided inputs. The training data set may includecharging trajectories, charge phase data, as well as an offlinemeasurement data set including voltage, current, and temperature of thebattery cell 103. The method may include offline training the ML model104 with the training data set.

The method may include setting a target, a predetermined target, or apredetermined value for the variable. The variable may be an internalstate of the battery cell 103 along the SOC trajectory such as reactionoverpotential during charging. The method may further include setting anindicator for the target. The method may further include providing theindicator variable to the ML model 104.

The method may further include connecting or bringing the ML model 104online once the ML model 104 is trained with the generated training dataset offline. The method may include providing the trained ML model asinput to the controller 108. The method may further include providingone or more operating conditions or real time measurements of the cell103 to the ML model once the ML model 104 is connected online, as FIG. 3schematically depicts. The generated training data set and/or the realtime measurements or the one or more operating conditions providesufficient information to the ML model 104 or the controller 108 toaccurately estimate the indicator value online along the chargingtrajectory. Once the estimated indicator is determined by the ML model104, the method may include supplying, by the ML model 104, theestimated indicator to the controller 108.

The method may include executing machine instructions by the controller108, the machine instructions implementing the following functions:receiving the one or more operating conditions and a trained ML model,outputting an indicator value along an SOC trajectory in response to theone or more operating conditions and the trained ML model to control afast charge state of the Li-ion cell from a current source, determininga first indicator value at time (t), determining a second indicatorvalue at time (t+1), or the like.

The method may further include matching, by the controller 108, theestimated indicator to the predetermined target. The method may furtherinclude receiving, by the controller 108, one or more controller inputs.The one or more controller inputs may include the one or more operatingconditions or one or more real time measurements from one or moresensors 112, the trained ML model, the indicator estimate from the MLmodel 104, the like, or a combination thereof. The method may furtherinclude outputting a control output from the controller 108 in the formof an indicator value along a state of charge (SOC) trajectory or anamount of current to be supplied from a current source to the cell 103to accomplish a fast charge in the minimum amount of time with minimaldamage to the cell 103.

The method may also include measuring one or more operating conditionssuch as voltage, current, capacity, internal temperature of the cell103, ambient temperature, and/or other properties of the cell 103 and/orthe system 100. The measuring may be conducted once, at least one time,once or more, continuously, discontinuously, randomly, regularly,during, before, and/or after the fast charge process, in real time,online, offline, or a combination thereof. The method may furtherinclude collecting the measurements at least once or more times, in aregular or irregular interval, continuously, or discontinuously from theone or more sensors 112. The method may include providing or supplyingthe measurements to the model 106. The method may include providing thereal time measurements as real time input to the ML model 104, the BMS102, the controller 108, or a combination thereof.

The method may include generating the estimated indicator value once, atleast one time, once or more, continuously, discontinuously, randomly,regularly, during, before, and/or after the fast charge process. Thegenerating may be in response to the ML model 104, and/or the controller108 receiving a new set of inputs such as one or more operatingconditions, measurements of voltage, current, and/or temperature of thecell 103, advance in learning of the ML model 104, or both.

The method may include further learning of the ML model 104 from thereal time or offline inputs from the specific cell 103, external data,additional training data set, or a combination thereof before, during,and/or after the fast charge process.

The method may include initiating and/or stopping to output the controloutput to a current source. The method may include initiating and/orstopping a flow of current from the current source of the cell 103. Themethod may include adjusting, increasing, decreasing, changing,maintaining, or regulating the amount of current being supplied to thecell 103 during the charge process.

The method may include fast charging the cell 103 to a predeterminedvalue. The predetermined value may be about 40, 50, 55, 60, 65, 70, 75,80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100% SOC or at fullcapacity. Capacity refers to the total amount of charge capable to bedrawn from the cell until the cell is depleted. A fully charged cell hasan SOC of 100%.

A non-limiting example of a sequence of the method steps is depicted inFIG. 5. FIG. 5 schematically shows the process 500 including the MLtraining process steps 502 and 504, the estimating of the indicator insteps 506 to 514, and the fast charge process of steps 516 to 524.

The processes, methods, or algorithms disclosed herein can bedeliverable to/implemented by a processing device, controller, orcomputer, which can include any existing programmable electronic controlunit or dedicated electronic control unit. Similarly, the processes,methods, or algorithms can be stored as data and instructions executableby a controller or computer in many forms including, but not limited to,information permanently stored on non-writable storage media such as ROMdevices and information alterably stored on writeable storage media suchas floppy disks, magnetic tapes, CDs, RAM devices, and other magneticand optical media. The processes, methods, or algorithms can also beimplemented in a software executable object. Alternatively, theprocesses, methods, or algorithms can be embodied in whole or in partusing suitable hardware components, such as Application SpecificIntegrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs),state machines, controllers or other hardware components or devices, ora combination of hardware, software and firmware components.

While exemplary embodiments are described above, it is not intended thatthese embodiments describe all possible forms encompassed by the claims.The words used in the specification are words of description rather thanlimitation, and it is understood that various changes can be madewithout departing from the spirit and scope of the disclosure. Aspreviously described, the features of various embodiments can becombined to form further embodiments of the invention that may not beexplicitly described or illustrated. While various embodiments couldhave been described as providing advantages or being preferred overother embodiments or prior art implementations with respect to one ormore desired characteristics, those of ordinary skill in the artrecognize that one or more features or characteristics can becompromised to achieve desired overall system attributes, which dependon the specific application and implementation. These attributes caninclude, but are not limited to cost, strength, durability, life cyclecost, marketability, appearance, packaging, size, serviceability,weight, manufacturability, ease of assembly, etc. As such, to the extentany embodiments are described as less desirable than other embodimentsor prior art implementations with respect to one or morecharacteristics, these embodiments are not outside the scope of thedisclosure and can be desirable for particular applications.

What is claimed is:
 1. A Li-ion battery management system comprising: aLi-ion cell; one or more sensors configured to sense one or moreoperating conditions of the Li-ion cell; a controller havingnon-transitory memory for storing machine instructions that are to beexecuted by the controller and operatively connected to the Li-ion cell,the machine instructions when executed by the controller implement thefollowing functions: receive first values of the one or more operatingconditions at time (t) and second values of the one or more operatingconditions at time (t+1) and a trained machine learning (ML) modelincluding a first state of charge (SOC) trajectory at a first stage of afast charge process and a second SOC trajectory at a second stage of thefast charge process; and output a first indicator value along the firstSOC trajectory in response to the first values of the one or moreoperating conditions and the trained ML model and a second indicatorvalue along the second SOC trajectory in response to the second valuesof the one or more operating conditions and the trained ML model tocontrol the fast charge process of the Li-ion cell from a currentsource.
 2. The Li-ion battery management system of claim 1, wherein theSOC trajectory includes a constant current (CC) phase, a constantindicator (CI) phase, and/or a constant voltage (CV) charge phase. 3.The Li-ion battery management system of claim 1, wherein the first andsecond indicator values are first and second states of the Li-ion cellalong the first and second SOC trajectories.
 4. The Li-ion batterymanagement system of claim 1, wherein the first and second indicatorvalues are overpotential values.
 5. The Li-ion battery management systemof claim 1, wherein the one or more operating conditions includes one ormore of an internal cell temperature, a cell voltage and a current. 6.The Li-ion battery management system of claim 1, wherein the first SOCtrajectory is different than the second SOC trajectory.
 7. The Li-ionbattery management system of claim 6, wherein the first stage of thefast charge process is earlier than the second stage of the fast chargeprocess.
 8. The Li-ion battery management system of claim 1, wherein thetrained ML model includes a training data set, the training data setincludes a plurality of individual charge trajectories, and eachtrajectory is generated by the trained ML model for a specificcombination of model parameters, initial conditions, and/or ambienttemperatures.
 9. The Li-ion battery management system of claim 1,wherein the trained ML model is a physics-based trained model.
 10. Amethod of fast charging a Li-ion battery cell, the method comprising:sensing one or more operating conditions of the Li-ion cell; receivingfirst values of the one or more operating conditions at time (t) andsecond values of the one or more operating conditions at time (t+1) anda trained machine learning (ML) model including a first state of charge(SOC) trajectory at a first stage of a fast charge process and a secondSOC trajectory at a second stage of the fast charge process; andoutputting a first indicator value along the first SOC trajectory inresponse to the first values of the one or more operating conditions andthe trained ML model and a second indicator value along the second SOCtrajectory in response to the second values of the one or more operatingconditions and the trained ML model to control the fast charge processof the Li-ion cell from a current source.
 11. The method of claim 10,wherein the SOC trajectory includes a constant current (CC) phase, aconstant indicator (CI) phase, and/or a constant voltage (CV) chargephase.
 12. The method of claim 10, wherein the first and secondindicator values are first and second states of the Li-ion cell alongthe first and second SOC trajectories.
 13. The method of claim 10,wherein the first and second indicator values are overpotential values.14. The method of claim 10, wherein the one or more operating conditionsincludes one or more of an internal cell temperature, a cell voltage,and a current.
 15. The method of claim 10, wherein the trained ML modelincludes a training set including a plurality of individual chargetrajectories, and further comprising generating each trajectory by thetrained ML model for a specific combination of model parameters, initialconditions, and/or ambient temperatures.
 16. A method of estimating abattery target indicator value for a Li-ion cell, the method comprising:training a machine learning (ML) model including a training data setincluding: a plurality of individual charge trajectories including afirst state of charge (SOC) trajectory at a first stage of a fast chargeprocess and a second SOC trajectory at a second stage of the fast chargeprocess, wherein each trajectory is generated by the trained ML modelfor a specific combination of model parameters, initial conditions,and/or ambient temperatures, a charge phase variable, a measurement dataset, and an indicator variable; sensing one or more operating conditionsof the Li-ion cell; receiving first values of the one or more operatingconditions at time (t) and second values of the one or more operatingconditions at time (t+1) and the trained ML model including the firstand second SOC trajectories; and outputting a first indicator valuealong the first SOC trajectory in response to the first values of theone or more operating conditions and the trained ML model and a secondindicator value along the second SOC trajectory in response to thesecond values of the one or more operating conditions and the trained MLmodel to control the fast charge process of the Li-ion cell from acurrent source.
 17. The method of claim 16, wherein the first and secondindicator values are first and second states of the Li-ion cell alongthe first and second SOC trajectories.
 18. The method of claim 16,further comprising supplying the training data set from a physics-basedmodel.
 19. The method of claim 16, wherein the measurement data setincludes one or more operating conditions of the Li-ion cell.
 20. Themethod of claim 16, wherein the one or more operating conditionsincludes one or more of an internal cell temperature, a cell voltage,and a current.